Vehicle control method, vehicle control device, and storage medium

ABSTRACT

A vehicle control method includes recognizing an object, generating a target trajectory of a vehicle, and automatically controlling driving of the vehicle on the basis of the target trajectory, calculating a region between a first virtual line, which passes through a reference point using the vehicle as a reference and a first point present in the vicinity of an outer edge of the object, and a second virtual line, which passes through the reference point and a second point present in the vicinity of the outer edge of the object, as a region through which the vehicle should avoid to travel, and excluding the target trajectory present in the traveling avoidance region, and automatically controlling the driving of the vehicle on the basis of a remaining target trajectory remained without being excluded.

CROSS-REFERENCE TO RELATED APPLICATION

Priority is claimed on Japanese Patent Application No. 2020-063527,filed Mar. 31, 2020, the content of which is incorporated herein byreference.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to a vehicle control method, a vehiclecontrol device, and a storage medium.

Description of Related Art

A technology of generating a target trajectory along which a vehicle isto travel in the future is known (for example, Japanese UnexaminedPatent Application, First Publication No. 2019-108124).

SUMMARY OF THE INVENTION

However, in the related art, in some cases, target trajectories that donot match the surrounding conditions may be generated. As a result,driving of the vehicle may not be safely controlled.

An aspect of the present invention is directed to providing a vehiclecontrol method, a vehicle control device, and a storage medium that arecapable of controlling driving of a vehicle more safely.

A vehicle control method, a vehicle control device, and a storage mediumaccording to the present invention employ the following configurations.

A first aspect of the present invention is a vehicle control method,including: recognizing an object present around a vehicle; generatingone or a plurality of target trajectories along which the vehicle is totravel on the basis of the recognized object; automatically controllingdriving of the vehicle on the basis of the generated target trajectory;calculating a region between a first virtual line, which passes througha reference point using the vehicle as a reference and a first pointpresent in the vicinity of an outer edge of the recognized object, and asecond virtual line, which passes through the reference point and asecond point present in the vicinity of the outer edge of the object anddifferent from the first point, as a traveling avoidance region that isa region through which traveling of the vehicle should be avoided, andexcluding the target trajectory present in the calculated travelingavoidance region from the one or the plurality of target trajectoriesthat were generated; and automatically controlling the driving of thevehicle on the basis of a remaining target trajectory remained withoutbeing excluded.

According to a second aspect, in the first aspect, the vehicle controlmethod further includes: optimizing shapes of the first virtual line andthe second virtual line on the basis of model prediction control.

According to a third aspect, in the first or second aspect, the vehiclecontrol method further includes calculating a risk region that is aregion of a risk distributed around the object, and inputting the riskregion to a model that determines the target trajectory according to therisk region, and generating the one or the plurality of targettrajectories on the basis of an output result of the model to which therisk region has been input.

According to a fourth aspect, in the third aspect, the model is amachine learning model learned to output the target trajectory when therisk region is input.

According to a fifth aspect, in the third or fourth aspect, the vehiclecontrol method further includes setting a position at which a potentialof the risk is lower than a threshold in the vicinity of the outer edgeof the object in the risk region as the first point and the secondpoint.

According to a sixth aspect, in any one of the first to fifth aspects,when the object recognized by the recognition part is a precedingvehicle that is traveling in front of the vehicle in a lane on which thevehicle is present, a setting of a region behind the preceding vehicleas the traveling avoidance region is not performed.

According to a seventh aspect, in any one of the first to sixth aspects,when a lane change of the vehicle is performed, a calculation of thetraveling avoidance region, and an exclusion of the target trajectorypresent in the traveling avoidance region from the one or the pluralityof target trajectories that were generated, are performed.

An eighth aspect is a vehicle control device including: a recognitionpart configured to recognize an object present around a vehicle; agenerating part configured to generate one or a plurality of targettrajectories along which the vehicle is to travel on the basis of theobject recognized by the recognition part; and a driving controllerconfigured to automatically control driving of the vehicle on the basisof the target trajectory generated by the generating part, wherein thegenerating part calculates a region between a first virtual line, whichpasses through a reference point using the vehicle as a reference and afirst point present in the vicinity of an outer edge of the objectrecognized by the recognition part, and a second virtual line, whichpasses through the reference point and a second point present in thevicinity of the outer edge of the object and different from the firstpoint, as a traveling avoidance region that is a region through whichtraveling of the vehicle should be avoided, and excludes the targettrajectory present in the calculated traveling avoidance region from theone or the plurality of target trajectories that were generated, and thedriving controller automatically controls driving of the vehicle on thebasis of a remaining target trajectory remained without being excludedby the generating part.

A ninth aspect is a program is provided to execute a computer mounted ona vehicle to: recognize an object present around the vehicle; generateone or a plurality of target trajectories along which the vehicle is totravel on the basis of the recognized object; automatically controldriving of the vehicle on the basis of the generated target trajectory;calculate a region between a first virtual line, which passes through areference point using the vehicle as a reference and a first pointpresent in the vicinity of an outer edge of the recognized object, and asecond virtual line, which passes through the reference point and asecond point present in the vicinity of the outer edge of the object anddifferent from the first point, as a traveling avoidance region that isa region through which traveling of the vehicle should be avoided, andexclude the target trajectory present in the calculated travelingavoidance region from the one or the plurality of target trajectoriesthat were generated; and automatically control the driving of thevehicle on the basis of a remaining target trajectory remained withoutbeing excluded.

According to any one of the above-mentioned aspects, it is possible tocontrol driving of a vehicle more safely.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a configuration view of a vehicle system using a vehiclecontrol device according to an embodiment.

FIG. 2 is a functional configuration view of a first controller, asecond controller, and a storage according to the embodiment.

FIG. 3 is a view for describing a risk region.

FIG. 4 is a view showing a variation in risk potential in a Y directionat a certain coordinate x1.

FIG. 5 is a view showing a variation in risk potential in a Y directionat a certain coordinate x2.

FIG. 6 is a view showing a variation in risk potential in a Y directionat a certain coordinate x3.

FIG. 7 is a view showing a variation in risk potential in an X directionat a certain coordinate y4.

FIG. 8 is a view showing a risk region determined by a risk potential.

FIG. 9 is a view schematically showing a generating method of a targettrajectory.

FIG. 10 is a view showing an example of a target trajectory output froma certain DNN model.

FIG. 11 is a flowchart showing an example of a series of processingflows by an automatic driving control device according to theembodiment.

FIG. 12 is a view showing an example of a situation that a host vehiclecan encounter.

FIG. 13 is a view showing an example of a plurality of targettrajectories.

FIG. 14 is a view showing an example of excluded target trajectories.

FIG. 15 is a view showing another example of a situation that a hostvehicle can encounter.

FIG. 16 is a view for describing a calculating method of a virtual line.

FIG. 17 is a view for describing an optimization method of a virtualline.

FIG. 18 is a view showing an example of a hardware configuration of anautomatic driving control device of the embodiment.

DETAILED DESCRIPTION OF THE INVENTION

Hereinafter, embodiments of a vehicle control device, a vehicle controlmethod, and a program of the present invention will be described withreference to the accompanying drawings. The vehicle control device ofthe embodiment is applied to, for example, an automatic travelingvehicle. Automatic traveling is, for example, controlling driving of thevehicle by controlling one or both of a speed or steering of thevehicle. The above-mentioned driving control of the vehicle includesvarious types of driving control, for example, an adaptive cruisecontrol system (ACC), a traffic jam pilot (TJP), auto lane changing(ALC), a collision mitigation brake system (CMBS) or a lane keepingassistance system (LKAS). Driving of the automatic traveling vehicle maybe controlled by manual driving of an occupant (a driver).

[Entire Configuration]

FIG. 1 is a configuration view of a vehicle system 1 using a vehiclecontrol device according to the embodiment. A vehicle on which thevehicle system 1 is mounted (hereinafter, referred to as a host vehicleM) is, for example, a two-wheeled, three-wheeled, or four-wheeledvehicle, and a driving source thereof is an internal combustion enginesuch as a diesel engine, a gasoline engine, or the like, an electricmotor, or a combination of these. The electric motor is operated usingan output generated by a generator connected to the internal combustionengine, or discharged energy of a secondary battery or a fuel cell.

The vehicle system 1 includes, for example, a camera 10, a radar device12, light detection and ranging (LIDAR) 14, an object recognition device16, a communication device 20, a human machine interface (HMI) 30, avehicle sensor 40, a navigation device 50, a map positioning unit (MPU)60, a driving operator 80, an automatic driving control device 100, atraveling driving power output device 200, a brake device 210, and asteering device 220. These devices or instruments are connected to eachother by a multiple communication line such as a controller area network(CAN) communication line or the like, a serial communication line, awireless communication network, or the like. The configuration shown inFIG. 1 is merely an example, a part of the configuration may be omitted,and another configuration may be added. The automatic driving controldevice 100 is an example of “a vehicle control device.”

The camera 10 is, for example, a digital camera using a solid-stateimage sensing device such as a charge coupled device (CCD), acomplementary metal oxide semiconductor (CMOS), or the like. The camera10 is attached to an arbitrary place of the host vehicle M. For example,when a side in front of the host vehicle M is imaged, the camera 10 isattached to an upper section of a front windshield, a rear surface of arearview mirror, or the like. In addition, when a side behind the hostvehicle M is imaged, the camera 10 is attached to an upper section of arear windshield, or the like. In addition, when the right side or theleft side of the host vehicle M is imaged, the camera 10 is attached toa right side surface or a left side surface of a vehicle body or a doormirror. The camera 10 images surroundings of the host vehicle M, forexample, periodically and repeatedly. The camera 10 may be a stereocamera.

The radar device 12 radiates radio waves such as millimeter waves or thelike to surroundings of the host vehicle M, and simultaneously, detectsthe radio waves (reflected waves) reflected by the object to detect aposition (a distance and an azimuth) of at least the object. The radardevice 12 is attached to an arbitrary place of the host vehicle M. Theradar device 12 may detect a position and a speed of the object using afrequency modulated continuous wave (FM-CW) method.

The LIDAR 14 radiates light to surroundings of the host vehicle M, andmeasures the scattered light of the radiated light. The LIDAR 14 detectsa distance to a target on the basis of a time from emission to receptionof light. The radiated light is, for example, a pulse-shaped laser beam.The LIDAR 14 is attached to an arbitrary place of the host vehicle M.

The object recognition device 16 recognizes a position, a type, a speed,or the like, of the object by performing sensor fusion processing withrespect to the detection result by some or all of the camera 10, theradar device 12, and the LIDAR 14. The object recognition device 16outputs the recognized result to the automatic driving control device100. In addition, the object recognition device 16 may output thedetection results of the camera 10, the radar device 12 and the LIDAR 14directly to the automatic driving control device 100. In this case, theobject recognition device 16 may be omitted from the vehicle system 1.

The communication device 20 uses, for example, a cellular network, aWi-Fi network, a Bluetooth (registered trademark), dedicated short rangecommunication (DSRC), or the like, comes in communication with anothervehicle present in the vicinity of the host vehicle M, or comes incommunication with various server devices via a radio base station.

The HMI 30 receives an input operation by an occupant in the hostvehicle M while providing various types of information to the occupant(including a driver). The HMI 30 may include, for example, a display, aspeaker, a buzzer, a touch panel, a microphone, a switch, a key, or thelike.

The vehicle sensor 40 includes a vehicle speed sensor configured todetect a speed of the host vehicle M, an acceleration sensor configuredto detect an acceleration, a yaw rate sensor configured to detect anangular speed around a vertical axis, and an azimuth sensor configuredto detect an orientation of the host vehicle M.

The navigation device 50 includes, for example, a global navigationsatellite system (GNSS) receiver 51, a navigation HMI 52, and a routedetermining part 53. The navigation device 50 holds first mapinformation 54 in a storage device such as a hard disk drive (HDD) aflash memory, or the like.

The GNSS receiver 51 specifies a position of the host vehicle M on thebasis of a signal received from the GNSS satellite. The position of thehost vehicle M may be specified or complemented by an inertialnavigation system (INS) that uses output of the vehicle sensor 40.

The navigation HMI 52 includes a display device, a speaker, a touchpanel, a key, and the like. The navigation HMI 52 may be partially orentirely shared with the HMI 30 described above. For example, theoccupant may input a destination of the host vehicle M to the navigationHMI 52 instead of (or in addition to) inputting the destination of thehost vehicle M to the HMI 30.

The route determining part 53 determines, for example, a route(hereinafter, a route on a map) from a position of the host vehicle Mspecified by the GNSS receiver 51 (or an input arbitrary position) to adestination input by an occupant using the HMI 30 or the navigation HMI52 with reference to the first map information 54.

The first map information 54 is, for example, information in which aroad shape is expressed by a link showing a road and a node connected bythe link. The first map information 54 may include a curvature of aroad, point of interest (POI) information, or the like. The route on amap is output to the MPU 60.

The navigation device 50 may perform route guidance using the navigationHMI 52 on the basis of the route on a map. The navigation device 50 maybe realized by, for example, a function of a terminal device such as asmart phone, a tablet terminal, or the like, held by the occupant. Thenavigation device 50 may transmit the current position and thedestination to the navigation server via the communication device 20,and acquire the same route as the route on a map from the navigationserver.

The MPU 60 includes, for example, a recommended lane determining part61, and holds second map information 62 in a storage device such as anHDD, a flash memory, or the like. The recommended lane determining part61 divides the route on a map provided from the navigation device 50into a plurality of blocks (for example, divided at each 100 [m] in anadvance direction of the vehicle), and determines a recommended lane ateach block with reference to the second map information 62. Therecommended lane determining part 61 performs determination of whichlane the vehicle travels from the left. The recommended lane determiningpart 61 determines a recommended lane such that the host vehicle M cantravel a reasonable route so as to reach a branch destination when adiverging place is present on the route on a map.

The second map information 62 is map information that has a higherprecision than the first map information 54. The second map information62 includes, for example, information of a center of a lane, informationof a boundary of a lane, or the like. In addition, the second mapinformation 62 may include road information, traffic regulationinformation, address information (address/zip code), facilityinformation, telephone number information, and the like. The second mapinformation 62 may be updated at any time by bringing the communicationdevice 20 in communication with another device.

The driving operator 80 includes, for example, an acceleration pedal, abrake pedal, a shift lever, a steering wheel, a modified steer, ajoystick, and other operators. A sensor configured to detect anoperation amount or existence of an operation is attached to the drivingoperator 80, and the detection result is output to some or all of theautomatic driving control device 100, the traveling driving power outputdevice 200, the brake device 210, and the steering device 220.

The automatic driving control device 100 includes, for example, a firstcontroller 120, a second controller 160, and a storage 180. The firstcontroller 120 and the second controller 160 are realized by executing aprogram (software) using a hardware processor such as a centralprocessing unit (CPU), a graphics processing unit (GPU) or the like.Some or all of these components may be realized by hardware (a circuitpart; including a circuitry) such as large scale integration (LSI), anapplication specific integrated circuit (ASIC), a field-programmablegate array (FPGA), or the like, or may be realized by cooperation ofsoftware and hardware. The program may be previously stored in a storagedevice (a storage device including a non-transient storage medium) suchas an HDD, a flash memory, or the like, of the automatic driving controldevice 100, stored in a detachable storage medium such as a DVD, aCD-ROM, or the like, or installed on an HDD or a flash memory of theautomatic driving control device 100 by mounting a storage medium (anon-transient storage medium) on a drive device.

The storage 180 is realized by the above-mentioned various types ofstorage devices. The storage 180 is realized by, for example, an HDD, aflash memory, an electrically erasable programmable read only memory(EEPROM), a read only memory (ROM), a random access memory (RAM), or thelike. Deep neural network (DNN(s)) model data 182 or the like is storedin the storage 180, for example, in addition to the program read andexecuted by the processor. The DNN model data 182 will be describedbelow in detail.

FIG. 2 is a functional configuration view of the first controller 120,the second controller 160 and the storage 180 according to theembodiment. The first controller 120 includes, for example, arecognition part 130 and an action plan generating part 140.

The first controller 120 realizes, for example, both of a function ofartificial intelligence (AI) and a function of a previously providedmodel at the same time. For example, a function of “recognizing acrossroad” is executed parallel to recognition of a crossroad throughdeep learning or the like and recognition based on a previously providedcondition (a signal that enables matching of patterns, road markings, orthe like), and may be realized by scoring and comprehensively evaluatingthem. Accordingly, reliability of automatic driving is guaranteed.

The recognition part 130 recognizes a situation or an environment ofsurroundings of the host vehicle M. For example, the recognition part130 recognizes an object present around the host vehicle M on the basisof information input from the camera 10, the radar device 12, and theLIDAR 14 via the object recognition device 16. The object recognized bythe recognition part 130 includes, for example, a bicycle, anmotorcycle, a four-wheeled automobile, a pedestrian, road signs, roadmarkings, road marking lines, electric poles, a guardrail, fallingobjects, or the like. In addition, the recognition part 130 recognizes astate such as a position, a speed, an acceleration, or the like, of theobject. The position of the object is recognized as a position onrelative coordinates (i.e., a relative position with respect to the hostvehicle M) using, for example, a representative point (a center ofgravity, a driving axial center, or the like) of the host vehicle M asan origin, and is used for control. The position of the object may bedisplayed at a representative point such as a center of gravity, acorner, or the like, of the object, or may be displayed as an expressedregion. The “state” of the object may include an acceleration, a jerk,or “an action state” (for example, whether a lane change is performed orto be performed) of the object.

In addition, the recognition part 130 recognizes, for example, a lane inwhich the host vehicle M is traveling (hereinafter, a host lane), aneighboring lane adjacent to the host lane, or the like. For example,the recognition part 130 recognizes a space between road marking linesas a host lane or a neighboring lane by comparing a pattern (forexample, arrangement of solid lines and broken lines) of road markinglines obtained from the second map information 62 with a pattern of roadmarking lines around the host vehicle M recognized from an imagecaptured by the camera 10.

In addition, the recognition part 130 may recognize a lane such as ahost lane or a neighboring lane by recognizing traveling lane boundaries(road boundaries) including road marking lines, road shoulders,curbstones, median strips, guardrails, and the like, while not beinglimited to road marking lines. In the recognition, the position of thehost vehicle M acquired from the navigation device 50 or a processingresult by the INS may be added. In addition, the recognition part 130may recognize a temporary stop line, an obstacle, a red signal, atollgate, and other road events.

The recognition part 130 recognizes a relative position or an attitudeof the host vehicle M with respect to the host lane when the host laneis recognized. The recognition part 130 may recognize a separation froma lane center of a reference point of the host vehicle M and an angle ofthe host vehicle M with respect to a line that connects a lane center inan advance direction as a relative position and an attitude of the hostvehicle M with respect to the host lane. Instead of this, therecognition part 130 may recognize a position or the like of a referencepoint of the host vehicle M with respect to any side end portion (a roadmarking line or a road boundary) of the host lane as a relative positionof the host vehicle M with respect to the host lane.

The action plan generating part 140 includes, for example, an eventdetermining part 142, a risk region calculating part 144 and a targettrajectory generating part 146.

The event determining part 142 determines a traveling aspect ofautomatic traveling when the host vehicle M is automatically travelingon a route in which a recommended lane is determined. Hereinafter,information that defines a traveling aspect of the automatic travelingwill be described while being referred to as an event.

The event includes, for example, a fixed speed traveling event, afollowing traveling event, a lane change event, a diverging event, amerging event, a take-over event, or the like. The fixed speed travelingevent is a traveling aspect of causing the host vehicle M to travelalong the same lane at a fixed speed. The following traveling event is atraveling aspect of causing the host vehicle M to follow another vehiclepresent within a predetermined distance in front of the host vehicle Mon the host lane (for example, within 100 [m]) and closest to the hostvehicle M (hereinafter, referred to as a preceding vehicle).

“Following” may be, for example, a traveling aspect of maintaining aconstant inter-vehicle distance (a relative distance) between the hostvehicle M and a preceding vehicle, or may be a traveling aspect ofcausing the host vehicle M to travel along a center of the host lane, inaddition to maintaining a constant inter-vehicle distance between thehost vehicle M and the preceding vehicle.

The lane change event is a traveling aspect of causing the host vehicleM to change lanes from the host lane to a neighboring lane. Thediverging event is a traveling aspect of causing the host vehicle M tomove to a lane on the side of the destination at a diverging point ofthe road. The merging event is a traveling aspect of causing the hostvehicle M to join a main line at a merging point. The take-over event isa traveling aspect of terminating automatic traveling and switchingautomatic driving to manual driving.

In addition, the event may include, for example, an overtaking event, anavoiding event, or the like. The overtaking event is a traveling aspectof causing the host vehicle M to change lane to a neighboring lanetemporarily, overtake the preceding vehicle in the neighboring lane andchange lane to the original lane again. The avoiding event is atraveling aspect of causing the host vehicle M to perform at least oneof braking and steering to avoid an obstacle present in front of thehost vehicle M.

In addition, for example, the event determining part 142 may change theevent already determined with respect to the current section to anotherevent, or determine a new event with respect to the current sectionaccording to a situation of the surroundings recognized by therecognition part 130 when the host vehicle M is traveling.

The risk region calculating part 144 calculates a risk regionpotentially distributed or present around the object recognized by therecognition part 130 (hereinafter, referred to as a risk region RA). Therisk is, for example, a risk that an object exerts an influence on thehost vehicle M. More specifically, the risk may be a risk of the hostvehicle M being caused to brake suddenly because a preceding vehicle hassuddenly slowed down or another vehicle has cut in front of the hostvehicle M from a neighboring lane, or may be a risk of the host vehicleM being forced to be steered suddenly because a pedestrian or a bicyclehas entered the roadway. In addition, the risk may be a risk that thehost vehicle M will exert an influence on the object. Hereinafter, thelevel of such risk is treated as a quantitative index value, and theindex value which will be described below is referred to as “a riskpotential p”.

FIG. 3 is a view for explaining the risk region RA. LN1 in the drawingdesignates a road marking line that partitions off the host lane on oneside, and LN2 designates a road marking line that partitions off thehost lane on the other side and a road marking line that partitions offa neighboring lane on one side. LN3 designates the other road markingline that divides the neighboring lane. In the plurality of road markinglines, LN1 and LN3 designate roadway edge markings, and LN2 designates acenter line that vehicles are allowed to pass beyond when overtaking. Inaddition, in the example shown, a preceding vehicle m1 is present infront of the host vehicle M on the host lane. X in the drawingdesignates a direction in which the vehicle is advancing, Y designates awidthwise direction of the vehicle, and Z designates a verticaldirection.

In the case of the situation shown, in the risk region RA, the riskregion calculating part 144 increases the risk potential p in a regionclose to the roadway edge markings LN1 and LN3, and decreases the riskpotential p in a region distant from the roadway edge markings LN1 andLN3.

In addition, in the risk region RA, the risk region calculating part 144increases the risk potential p as it approaches the region close to thecenter line LN2 and decreases the risk potential p as it approaches theregion far from the center line LN2. Since the center line LN2 isdifferent from the roadway edge markings LN1 and LN3 and the vehicle isallowed to deviate from the center line LN2, the risk region calculatingpart 144 causes the risk potential p with respect to the center line LN2to be lower than the risk potential p with respect to the roadway edgemarkings LN1 and LN3.

In addition, in the risk region RA, the risk region calculating part 144increases the risk potential p as it approaches a region close to thepreceding vehicle m1 that is one of an object, and decreases the riskpotential p as it approaches a region far from the preceding vehicle m1.That is, in the risk region RA, the risk region calculating part 144 mayincrease the risk potential p as a relative distance between the hostvehicle M and the preceding vehicle m1 becomes shorter, and may decreasethe risk potential p as the relative distance between the host vehicle Mand the preceding vehicle m1 becomes longer. Here, the risk regioncalculating part 144 may increase the risk potential p as an absolutespeed or an absolute acceleration of the preceding vehicle m1 isincreased. In addition, the risk potential p may be appropriatelydetermined according to a relative speed or a relative accelerationbetween the host vehicle M and the preceding vehicle m1, a time tocollision (TTC), or the like, instead of or in addition to the absolutespeed or the absolute acceleration of the preceding vehicle m1.

FIG. 4 is a view showing variation in the risk potential p in the Ydirection at a certain coordinate x1. Here, y1 in the drawing designatesa position (coordinate) of the roadway edge marking LN1 in the Ydirection, y2 designates a position (coordinate) of the center line LN2in the Y direction, and y3 designates a position (coordinate) of theroadway edge marking LN3 in the Y direction.

As shown, the risk potential p is highest in the vicinity of thecoordinates (x1, y1) at which the roadway edge marking LN1 is present orin the vicinity of the coordinates (x1, y3) at which the roadway edgemarking LN3 is present, and the risk potential p is the second highestin the vicinity of the coordinates (x1, y2) at which the center line LN2is present after the coordinates (x1, y1) or (x1, y3). As describedabove, in a region in which the risk potential p is equal to or greaterthan a previously determined threshold Th, in order to prevent thevehicle from entering the region, the target trajectory TR is notgenerated.

FIG. 5 is a view showing variation in the risk potential p in the Ydirection at a certain coordinate x2. The coordinate x2 is closer to thepreceding vehicle m1 than the coordinate x1 is. For this reason,although the preceding vehicle m1 is not present in the region betweenthe coordinates (x2, y1) at which the roadway edge marking LN1 ispresent and the coordinates (x2, y2) at which the center line LN2 ispresent, a risk can be considered such as the sudden deceleration of thepreceding vehicle m1 or the like. As a result, the risk potential p inthe region between (x2, y1) and (x2, y2) tends to be higher than therisk potential p in the region between (x1, y1) and (x1, y2), forexample, the threshold Th or more.

FIG. 6 is a view showing a variation in the risk potential p in the Ydirection at a certain coordinate x3. The preceding vehicle m1 ispresent at the coordinate x3. For this reason, the risk potential p inthe region between the coordinates (x3, y1) at which the roadway edgemarking LN1 is present and the coordinates (x3, y2) at which the centerline LN2 is present is higher than the risk potential p in the regionbetween (x2, y1) and (x2, y2), and is equal to or greater than thethreshold Th.

FIG. 7 is a view showing a variation in the risk potential p in the Xdirection at a certain coordinate y4. The coordinate y4 is intermediatecoordinates between y1 and y2, and the preceding vehicle m1 is presentat the coordinate y4. For this reason, the risk potential p is highestat the coordinates (x3, y4), the risk potential p at the coordinates(x2, y4) farther from the preceding vehicle m1 than the coordinates (x3,y4) is lower than the risk potential p at the coordinates (x3, y4), andthe risk potential p at the coordinates (x1, y4) farther from thepreceding vehicle m1 than the coordinates (x2, y4) is lower than therisk potential p at the coordinates (x2, y4).

FIG. 8 is a view showing the risk region RA determined by the riskpotential p. As shown, in the risk region calculating part 144, the riskregion RA is divided into a plurality of mesh squares (also referred toas grid squares), and the risk potential p is associated with each ofthe plurality of mesh squares. For example, the risk potentialcorresponds to the mesh square (x_(i), y_(i)). That is, the risk regionRA is expressed by a data structure referred to as a vector or a tensor.

The risk region calculating part 144 normalizes the risk potential p ofeach mesh when the risk potential p corresponds to each of the pluralityof meshes.

For example, the risk region calculating part 144 may normalize the riskpotential p such that the maximum value of the risk potential p is 1 andthe minimum value is 0. Specifically, the risk region calculating part144 selects the risk potential p_(max) that becomes the maximum valueand the risk potential p_(min) that becomes the minimum value among therisk potential p of all of the meshes included in the risk region RA.The risk region calculating part 144 selects one mesh (x_(i), y_(i)) ofinterest from all of the meshes included in the risk region RA,subtracts the minimum risk potential p_(min) from the risk potentialp_(ij) corresponding to the mesh (x_(i), y_(i)), subtracts the minimumrisk potential p_(min) from the maximum risk potential p_(max), anddivides (p_(ij)−p_(min)) by (p_(max)−p_(min)). The risk regioncalculating part 144 repeats the above-mentioned processing whilechanging the mesh of interest. Accordingly, the risk region RA isnormalized such that the maximum value of the risk potential p is 1 andthe minimum value is 0.

In addition, the risk region calculating part 144 may calculate anaverage value μ and a standard deviation σ of the risk potential p ofall the meshes included in the risk region RA, subtract the averagevalue μ from the risk potential p_(ij) corresponding to the mesh (x_(i),y_(i)), and divide (p_(ij)−μ) by the standard deviation 6. Accordingly,the risk region RA is normalized such that the maximum value of the riskpotential p is 1 and the minimum value is 0.

In addition, the risk region calculating part 144 may normalize the riskpotential p such that the maximum value of the risk potential p is anarbitrary M and the minimum value is an arbitrary m. Specifically, when(A, the risk region (p_(ij)−p_(min))/(p_(max)−p_(min)) is calculatingpart 144 multiplies A by (M-m), and adds m to A (M-m). Accordingly, therisk region RA is normalized such that the maximum value of the riskpotential p becomes M and the minimum value becomes m.

Returning to the description in FIG. 2 , the target trajectorygenerating part 146 generates the future target trajectory TR to allowthe host vehicle M to automatically travel (independently of anoperation of the driver) in the traveling aspect defined by the event,such that the host vehicle M is able to travel in the recommended lanedetermined by the recommended lane determining part 61, and further, thehost vehicle M is able to respond to the surrounding situation whentraveling in the recommended lane. The target trajectory TR includes,for example, a position element that determines the position of the hostvehicle M in the future, and a speed element that has determined thespeed or the like of the host vehicle M in the future.

For example, the target trajectory generating part 146 determines aplurality of points (trajectory points) at which the host vehicle Mshould arrive in sequence as position elements of the target trajectoryTR. The trajectory point is a point at which the host vehicle M shouldarrive after each of predetermined traveling distances (for example,about every several [m]). The predetermined traveling distance may becalculated, for example, according to a distance along a road whentraveling on a route.

In addition, the target trajectory generating part 146 determines atarget speed v and a target acceleration a for every predeterminedsampling time (for example, about every several fractions of a [sec]) asa speed element of the target trajectory TR. In addition, the trajectorypoint may be a position at which the host vehicle M is to arrive in asampling time for every predetermined sampling time. In this case, thetarget speed v or the target acceleration a is determined according toan interval of the sampling times and the trajectory points.

For example, the target trajectory generating part 146 reads the DNNmodel data 182 from the storage 180, and generates one or a plurality oftarget trajectories TR using a model defined by the data.

The DNN model data 182 is information (a program or a data structure)defined by one or a plurality of DNN models MDL1. The DNN model MDL1 isa deep learning model learned to output the target trajectory TR whenthe risk region RA is input. Specifically, the DNN models MDL1 may be aconvolutional neural network (CNN), a recurrent neural network (RNN), ora combination of these. The DNN model data 182 includes, for example,various types of information such as coupling information showing howunits included in the plurality of layers constituting the neuralnetwork are coupled to each other, a coupling coefficient applied to thedata input and output between the coupled units, or the like. The DNNmodel MDL1 is an example of “a machine learning model.”

The coupling information includes, for example, information thatdesignates the number of units included in each layer, a type of a unitof destinations of the units, or the like, and information of anactivation function of each unit, a gate provided between the units ofthe hidden layer, or the like. The activation function may be, forexample, a normalized linear function (an ReLU function), or may be aSigmoid function, a step function, or the other functions. The gateselectively passes or weights the data transmitted between the units,for example, depending on a value (for example, 1 or 0) returned by theactivation function. The coupling coefficient includes, for example, aweight coefficient given to the output data when the data is output froma unit of a certain layer to a unit of a deeper layer in the hiddenlayer of the neural network. In addition, the coupling coefficient mayinclude a bias element peculiar to each layer.

The DNN model MDL1 is sufficiently learned on the basis of, for example,instructor data. The instructor data is, for example, a data set inwhich the target trajectory TR, which is a correct answer that the DNNmodel MDL1 should output, is associated with the risk region RA as aninstructor label (also referred to as a target). That is, the instructordata is a data set in which the risk region RA that is input data andthe target trajectory TR that is output data are combined. The targettrajectory TR, which is the correct answer, may be, for example, thetarget trajectory that passes the mesh having the lowest risk potentialp, which is less than the threshold Th, among the plurality of meshescontained in the risk region RA. In addition, the target trajectory TR,which is the correct answer, may be, for example, an actual trajectoryof a vehicle driven by a driver in a certain risk region RA.

The target trajectory generating part 146 inputs the risk region RAcalculated by the risk region calculating part 144 to each of theplurality of DNN models MDL1, and generates one or a plurality of targettrajectories TR on the basis of the output result of the DNN models MDL1to which the risk region RA is input.

FIG. 9 is a view schematically showing a method of generating the targettrajectory TR. For example, the target trajectory generating part 146inputs a vector or a tensor representing the risk region RA to each ofthe plurality of DNN models MDL1. In the example shown, the risk regionRA is represented as a second-order tensor with m rows and n columns.Each of the DNN models MDL1 to which the vector or the tensorrepresenting the risk region RA is input outputs one target trajectoryTR. The target trajectory TR is represented by, for example, a vector ora tensor including a plurality of elements such as a target speed v, atarget acceleration a, a displacement amount u of steering, and acurvature κ of a track.

FIG. 10 is a view showing an example of the target trajectory TR outputfrom a certain DNN model MDL1. Like the example shown, since the riskpotential p around the preceding vehicle m1 is increased, the targettrajectory TR is generated to avoid it. As a result, the host vehicle Mchanges the lane to a neighboring lane partitioned by road marking linesLN2 and LN3, and overtakes the preceding vehicle m1.

Returning to the description of FIG. 2 , the second controller 160controls the traveling driving power output device 200, the brake device210, and the steering device 220 such that the host vehicle M passesthrough the target trajectory TR generated by the target trajectorygenerating part 146 on time as scheduled. The second controller 160includes, for example, a first acquisition part 162, a speed controller164 and a steering controller 166. The second controller 160 is anexample of “a driving controller.”

The first acquisition part 162 acquires the target trajectory TR fromthe target trajectory generating part 146, and stores the acquiredtarget trajectory TR in a memory of the storage 180.

The speed controller 164 controls one or both of the traveling drivingpower output device 200 and the brake device 210 on the basis of thespeed element (for example, the target speed v, the target accelerationa, or the like) included in the target trajectory TR stored in thememory.

The steering controller 166 controls the steering device 220 accordingto the position element included in the target trajectory stored in thememory (for example, a curvature κ of the target trajectory, adisplacement amount u of the steering according to the position of thetrajectory point, or the like).

Processing of the speed controller 164 and the steering controller 166is realized by, for example, a combination of feedforward control andfeedback control. As an example, the steering controller 166 combinesand executes the feedforward control according to the curvature of theroad in front of the host vehicle M and the feedback control based onthe separation from the target trajectory TR.

The traveling driving power output device 200 outputs a travelingdriving power (torque) to a driving wheel in order to cause the vehicleto travel. The traveling driving power output device 200 includes, forexample, a combination of an internal combustion engine, an electricmotor, and a gearbox, and a power electronic control unit (ECU)configured to control them. The power ECU controls the configurationaccording to the information input from the second controller 160 or theinformation input from the driving operator 80.

The brake device 210 includes, for example, a brake caliper, a cylinderconfigured to transmit a hydraulic pressure to the brake caliper, anelectric motor configured to generate a hydraulic pressure in thecylinder, and a brake ECU. The brake ECU controls the electric motoraccording to the information input from the second controller 160 or theinformation input from the driving operator 80 such that a brake torqueaccording to the braking operation is output to the wheels. The brakedevice 210 may include a mechanism configured to transmit a hydraulicpressure, which is generated by an operation of the brake pedal includedin the driving operator 80, to the cylinder via the master cylinder as abackup. Further, the brake device 210 is not limited to theabove-mentioned configuration and may be an electronically controlledhydraulic brake device configured to control an actuator according tothe information input from the second controller 160 and transmit ahydraulic pressure of the master cylinder to the cylinder.

The steering device 220 includes, for example, a steering ECU and anelectric motor. The electric motor changes an orientation of a steeredwheel by, for example, applying a force to a rack and pinion mechanism.A steering ECU drives the electric motor and changes an orientation ofthe steered wheel according to the information input from the secondcontroller 160 or the information input from the driving operator 80.

[Processing Flow]

Hereinafter, a series of processing flows by the automatic drivingcontrol device 100 according to the embodiment will be described using aflowchart. FIG. 11 is a flowchart showing an example of a series ofprocessing flows by the automatic driving control device 100 accordingto the embodiment. Processing of the flowchart may be repeatedlyperformed at a predetermined period, for example, when the host vehicleM travels in the section determined through the lane change event by theevent determining part 142 (i.e., when the lane change is performed).That is, processing of the flowchart may be repeatedly performed at apredetermined period when it is desired to displace the position of thehost vehicle M in both of an advance direction X of the vehicle and alane width direction Y.

First, the recognition part 130 recognizes an object present on a roadalong which the host vehicle M is traveling (step S100). The object maybe various objects such as a road marking line on the road, apedestrian, and an oncoming vehicle, as described above.

Next, the risk region calculating part 144 calculates the risk region RAon the basis of a position or a type of the road marking line, aposition, a speed, an orientation, or the like, of another vehicletherearound (step S102).

For example, the risk region calculating part 144 divides a previouslydetermined range into a plurality of meshes and calculates the riskpotential p for each of the plurality of meshes. Then, the risk regioncalculating part 144 calculates the vector or the tensor correspondingto the risk potential p with respect to each mesh as the risk region RA.Here, the risk region calculating part 144 normalizes the risk potentialp.

Next, the target trajectory generating part 146 calculates a region inwhich the host vehicle M should avoid to travel (hereinafter, referredto as a traveling avoidance region AA) (step S104).

For example, the target trajectory generating part 146 calculates avirtual line (hereinafter, referred to as a virtual line VL) passingthrough a reference point Pref using the host vehicle as a reference andan arbitrary point Px present in the vicinity of an outer edge of theobject recognized by the recognition part 130, and calculates thevirtual line VL passing through the reference point Pref and the pointPy present in the vicinity of the outer edge of the object and differentfrom the point Px. Any one of the point Px and the point Py is anexample of “a first point” and the other is an example of “a secondpoint.”

The reference point may be, for example, an installation position or thelike of the camera 10 attached to the front. For example, when theobject is a polygonal shape such as a quadrangular shape, a hexagonalshape, or the like, the outer edge of the object may be an apex of thepolygonal shape. That is, when the object is a polygonal shape, “thevicinity of the outer edge” may be read as “the vicinity of the apex” ofthe polygonal shape. In addition, when the object is a circular shape,the outer edge of the object may be a point of contact of a circle. Thatis, when the object is a circular shape, “the vicinity of the outeredge” may be read as “the vicinity of the point of contact” of acircular shape.

In addition, the virtual line VL may be calculated in consideration ofthe risk region RA. Specifically, the virtual line VL may be calculatedso as to pass the position where the risk potential p is lower than athreshold th in the vicinity of the outer edge of the object and theabove-mentioned reference point Pref.

FIG. 12 is a view showing an example of a situation that the hostvehicle M can encounter. In the example shown, another vehicle m1 ispresent on a first traveling lane between the road marking lines LN1 andLN2, another vehicle m2 is present on a second traveling lane betweenthe road marking lines LN2 and LN3, and another vehicle m3 is present ona third traveling lane between the road marking lines LN3 and LN4. Thehost vehicle M is present on the second traveling lane, and all othervehicles are also ahead of the host vehicle M.

In such a situation, the target trajectory generating part 146 sets thepoint Px1 and the point Py1 at a position in the vicinity of theboundary between a scene and the other vehicle m1 when a left front sideis seen from the reference point Pref of the host vehicle M. Since theother vehicle m1 is a quadrangular shape, for example, in four corners(apexes) of the other vehicle m1, a left rear corner and a right frontcorner are outer edges of the other vehicle m1. Each corner of the othervehicle m1 is an example of “the vicinity of the outer edge of theobject.”

Accordingly, the target trajectory generating part 146 sets the pointPx1 in the vicinity of the left rear corner, and sets the point Py1 inthe vicinity of the right front corner. Here, the target trajectorygenerating part 146 may set the point Px1 and the point Py1 to aposition closest to the outer edge of the other vehicle m1 and at whichthe risk potential p is lower than the threshold th in consideration ofthe risk region RA. Accordingly, the point Px1 and the point Py1 can beset to a position slightly deviated from the outer edge of the othervehicle m1, i.e., the vicinity of the outer edge as described above.

The target trajectory generating part 146 calculates the virtual lineVL1 passing through the reference point Pref and the point Px1 andcalculates the virtual line VL2 passing through the reference point Prefand the point Py1 when the point Px1 and the point Py1 are set. Then,the target trajectory generating part 146 calculates a region betweenthe virtual line VL1 and the virtual line VL2 as a traveling avoidanceregion AA1.

The other vehicle m2 and m3 are also similar as described above. Forexample, the target trajectory generating part 146 sets the point Px2and the point Py2 to a position in the vicinity of the boundary betweenthe scenery and the other vehicle m2 when a forward side is seen fromthe reference point Pref of the host vehicle M. The outer edges of theother vehicle m2 are the left rear corner and the right rear corneramong the four corners of the other vehicle m2. Accordingly, the targettrajectory generating part 146 sets the point Px2 in the vicinity of theleft rear corner and sets the point Py2 in the vicinity of the rightrear corner. The target trajectory generating part 146 calculates thevirtual line VL3 passing through the reference point Pref and the pointPx2 and calculates the virtual line VL4 passing through the referencepoint Pref and the point Py2 when the point Px2 and the point Py2 areset. Then, the target trajectory generating part 146 calculates a regionbetween the virtual line VL3 and the virtual line VL4 as a travelingavoidance region AA2.

In addition, for example, the target trajectory generating part 146 setsthe point Px3 and the point Py3 to a position in the vicinity of theboundary between the scenery and the other vehicle m3 when a rightwardside is seen from the reference point Pref of the host vehicle M. Theouter edges of the other vehicle m3 are the left front corner and theright rear corner among the four corners of the other vehicle m3.Accordingly, the target trajectory generating part 146 sets the pointPx3 in the vicinity of the left front corner and sets the point Py3 inthe vicinity of the right rear corner. The target trajectory generatingpart 146 calculates the virtual line VL5 passing through the referencepoint Pref and the point Px3 and calculates the virtual line VL6 passingthrough the reference point Pref and the point Py3 when the point Px3and the point Py3 are set. Then, the target trajectory generating part146 calculates a region between the virtual line VL5 and the virtualline VL6 as a traveling avoidance region AA3.

Returning to description of the flowchart in FIG. 11 , next, the targettrajectory generating part 146 generates the plurality of targettrajectories TR using the plurality of DNN models MDL1 defined by theDNN model data 182 (step S106).

Next, the target trajectory generating part 146 excludes the targettrajectory TR present inside each of the traveling avoidance regions AAfrom the plurality of target trajectories TR that were generated, andleaves the target trajectory TR present outside the traveling avoidanceregion AA (step S108).

FIG. 13 is a view showing an example of the plurality of targettrajectories TR. For example, when the four DNN models MDL1 are definedby the DNN model data 182, the target trajectory generating part 146inputs the risk region RA calculated by the risk region calculating part144 through processing of S102 to each of the four DNN models MDL1. Inresponse to this, each of the DNN models MDL1 outputs one targettrajectory TR. That is, as shown, the total four target trajectories TRreferred to as TR1, TR2, TR3 and TR4 are generated.

As described above, the DNN models MDL1 are learned using instructordata in which the target trajectory TR, which is a correct answer (atrack passing a region in which the risk potential p is lower than thethreshold Th), is associated with the risk region RA as an instructorlabel. That is, a parameter such as a weight coefficient, a biaselement, or the like, of the DNN model MDL1 is determined using astochastic gradient descent method or the like such that a difference(an error) between the target trajectory TR output by the DNN model MDL1when a certain risk region RA is input and the correct answer targettrajectory TR associated with the risk region RA as an instructor labelis reduced.

For this reason, the DNN model MDL1 behaves as a kind of stochasticmodel. The target trajectory TR output by the DNN model MDL1 is expectedto be a track so as to pass a region having the risk potential p lowerthan the threshold Th. However, since the DNN model MDL1 determines thetarget trajectory TR probabilistically, its probability is extremelylow, but it cannot be denied that possibility of a track such as passinga region having the risk potential p higher than the threshold Th beinggenerated. That is, as shown, the target trajectory TR1 or TR4 may begenerated in which the host vehicle M will suddenly approach the othervehicle m1 or m2.

For this reason, the target trajectory generating part 146 determineswhether each of the generated target trajectories TR is present insideor outside the calculated traveling avoidance region AA, excludes thetarget trajectory TR present inside the traveling avoidance region AA,and leaves the target trajectory TR present outside the travelingavoidance region AA.

FIG. 14 is a view showing an example of the excluded target trajectoryTR. In the example shown, in the four target trajectories TR, TR1 ispresent inside the traveling avoidance region AA1 and TR4 is presentinside the traveling avoidance region AA3. In this case, the targettrajectory generating part 146 excludes the target trajectory TR1 andTR4.

Returning to description of the flowchart of FIG. 11 , next, the targettrajectory generating part 146 selects an optimal target trajectory TRfrom one or a plurality of target trajectories TR remained while notbeing excluded (step S110).

For example, the target trajectory generating part 146 may evaluate eachtarget trajectory TR from the viewpoint of smoothness of the targettrajectory TR or gradual acceleration/deceleration, and select thetarget trajectory TR with a higher evaluation as an optimal targettrajectory TR. More specifically, the target trajectory generating part146 may select the target trajectory TR having the smallest curvature κand the smallest target acceleration a as an optimal target trajectoryTR. Further, selection of the optimal target trajectory TR is notlimited thereto, and may be performed in consideration of otherviewpoints.

Then, target trajectory generating part 146 outputs the optimal targettrajectory TR to the second controller 160. In response to this, thesecond controller 160 controls at least one of the speed and thesteering of the host vehicle M on the basis of the optimal targettrajectory TR output by the target trajectory generating part 146 (stepS112). Accordingly, processing of the flowchart is terminated.

According to the above-mentioned embodiment, the automatic drivingcontrol device 100 recognizes various objects referred to as a roadmarking line, an oncoming vehicle, and a pedestrian present around thehost vehicle M, and calculates a region of a risk present around theobject potentially as the risk region RA. Further, the automatic drivingcontrol device 100 calculates the virtual line VL passing through thereference point Pref using the host vehicle M as a reference and thepoint Px present in the vicinity of the outer edge of the object, andthe virtual line VL passing through the reference point Pref and thepoint Py present in the vicinity of the outer edge of the object anddifferent from the point Px. The automatic driving control device 100calculates a region between the two virtual lines VL that werecalculated as the traveling avoidance region AA. The automatic drivingcontrol device 100 excludes the target trajectory TR present inside thecalculated traveling avoidance region AA from one or a plurality oftarget trajectories TR that were generated, and automatically controlsdriving of the host vehicle M on the basis of the target trajectory TRthat was remained without being excluded. Accordingly, driving of thehost vehicle M can be more stably controlled.

Variant of Embodiment

Hereinafter, a variant of the above-mentioned embodiment will bedescribed. In the above-mentioned embodiment, while the targettrajectory generating part 146 is described as outputting the targettrajectory TR to each of the plurality of DNN models MDL1 by inputtingthe risk region RA to each of the plurality of DNN models MDL1, it isnot limited thereto. For example, the target trajectory generating part146 may input the risk region RA to a certain one of the DNN modelsMDL1, and may cause the DNN models MDL1 to output the plurality oftarget trajectories TR. In this case, the DNN models MDL1 are learned onthe basis of the instructor data in which the plurality of targettrajectories TR, which are correct answers that should be output by theDNN models MDL1, are associated with a certain risk region RA as aninstructor label. Accordingly, the DNN models MDL1 outputs the pluralityof target trajectories TR when a certain risk region RA is input.

In addition, in the above-mentioned embodiment, while the precedingvehicle such as the other vehicle m2 has been described as calculatingthe traveling avoidance region AA, it is not limited thereto. Forexample, when the host vehicle M travels in a section in which not onlythe lane change event but also a following traveling event or anotherevent is planned, the target trajectory generating part 146 may notcalculate a region behind the preceding vehicle as the travelingavoidance region AA in the section.

FIG. 15 is a view showing another example of a situation that the hostvehicle M can encounter. For example, in the situation shown, when thehost vehicle M follows the other vehicle m2 that is a preceding vehicle,the target trajectory TR is a track extending from the host vehicle M tothe other vehicle m2. In this case, when the region behind the othervehicle m2 is set as the traveling avoidance region AA, the host vehicleM may not be able to follow the other vehicle m2. Accordingly, in thesituation in which the lane changes may not be made, the targettrajectory generating part 146 does not calculate the region behind thepreceding vehicle as the traveling avoidance region AA. Accordingly, itis possible to control the driving of the host vehicle M more safelywhile avoiding the situation in which the host vehicle M is stuck.

In addition, in the above-mentioned embodiment, while the targettrajectory generating part 146 calculates the virtual line VL to passthe vicinity of the outer edge of the object (for example, the vicinityof the corner of the vehicle) in order to calculate the travelingavoidance region AA, it is not limited thereto. For example, the targettrajectory generating part 146 may calculate the virtual line VL on thebasis of the risk potential p around the object.

FIG. 16 is a view for describing a calculation method of the virtualline VL. In the example shown, a calculation method of the virtual lineVL5 and VL6 with respect to the other vehicle m3 will be described.RA_(X) in the drawing designates a region in which the risk potential pis higher than the threshold th. As described above, the region closerto the other vehicle m3 has a higher risk potential p, and the regionnearer to the other vehicle m3 has lower risk potential p. From this,the region RA_(X) is a region larger than the other vehicle m3. Theinside of the region RA_(X) is regarded as a region through which thehost vehicle M should not pass because the risk potential p is high.Accordingly, the target trajectory generating part 146 may calculate astraight line that passes through the outer edge of the region RA_(X)which uses the other vehicle m3 as a reference (a boundary at which therisk potential p is equal to or smaller than the threshold th) as thevirtual lines VL5 and VL6. In other words, the target trajectorygenerating part 146 may calculate a straight line that passes through aposition separated from each corner of the other vehicle m3 when seenfrom the other vehicle m3 as the virtual line VL5 and VL6. Accordingly,the traveling avoidance region AA can be calculated dynamically whileconsidering the future movement of the other vehicle m3. The positionseparated from each corner of the other vehicle m3 is another example of“the vicinity of the outer edge of the object.”

In addition, the target trajectory generating part 146 may calculate thetraveling avoidance region AA while changing the virtual line VLcalculated on the basis of the risk potential p in the vicinity of theobject to an optimized shape using an optimization method such as modelpredictive control (MPC) or the like.

FIG. 17 is a view for describing an optimization method of the virtualline VL. For example, the risk potential p around the other vehicle m3is calculated at a certain initial cycle t₀, and virtual lines VL5 (t₀)and VL6 (t₀) with respect to the other vehicle m3 are calculated on thebasis of the risk potential p. In this case, the target trajectorygenerating part 146 may calculate the virtual lines VL5 and VL6 bysolving an arbitrary optimization problem whenever a processing cycle isrepeated and the risk potential p is calculated. Accordingly, as shown,virtual lines VL5 (t_(k)) and VL6 (t_(k)) at a certain cycle t_(k) canbe calculated as virtual lines with a non-linear shape rather than alinear shape, and the traveling avoidance region AA with an optimalshape having considering the future movement of the other vehicle m3 canbe calculated. As a result, since the optimal target trajectory TR canbe easily selected, driving of the host vehicle M can be more safelycontrolled.

In addition, the target trajectory generating part 146 of theabove-mentioned embodiment is not limited to the DNN, and may output thetarget trajectory TR to another machine learning model by inputting therisk region RA to a model using another machine learning as a base, forexample, a binary tree type model, a game tree type model, a model inwhich bottom layer neural networks are coupled to each other like aBoltzmann machine, a reinforcement learning model, a deep reinforcementlearning model, or the like. The binary tree type model, the game treetype model, the model in which bottom layer neural networks are coupledto each other like a Boltzmann machine, the reinforcement learningmodel, the deep reinforcement learning model, or the like, is anotherexample of “a machine learning model.”

[Hardware Configuration]

FIG. 18 is a view showing an example of a hardware configuration of theautomatic driving control device 100 of the embodiment. As shown, theautomatic driving control device 100 has a configuration in which acommunication controller 100-1, a CPU 100-2, a RAM 100-3 used as aworking memory, a ROM 100-4 configured to store a booting program or thelike, a storage device 100-5 such as a flash memory, an HDD, or thelike, a drive device 100-6, and the like, are connected to each other byan internal bus or a dedicated communication line. The communicationcontroller 100-1 performs communication with components other than theautomatic driving control device 100. A program 100-5 a executed by theCPU 100-2 is stored in the storage device 100-5. The program isdeveloped in the RAM 100-3 by a direct memory access (DMA) controller(not shown) or the like, and executed by the CPU 100-2. Accordingly,some or all of the first controller and the second controller 160 arerealized.

The above-mentioned embodiment can be expressed as follows.

A vehicle control device is configured to include:

at least one memory in which a program is stored; and

at least one processor,

wherein the processor executes the program to:

recognize an object present around a vehicle;

generate one or a plurality of target trajectories along which thevehicle is to travel on the basis of the recognized object;

automatically control driving of the vehicle on the basis of thegenerated target trajectory;

calculate a region between a first virtual line, which passes through areference point using the vehicle as a reference and a first pointpresent in the vicinity of an outer edge of the recognized object, and asecond virtual line, which passes through the reference point and asecond point present in the vicinity of the outer edge of the object anddifferent from the first point, as a traveling avoidance region that isa region through which traveling of the vehicle should be avoided, andexclude the target trajectory present in the calculated travelingavoidance region from the one or the plurality of target trajectoriesthat were generated; and

automatically control the driving of the vehicle on the basis of thetarget trajectory remained while being not excluded.

While preferred embodiments of the invention have been described andillustrated above, it should be understood that these are exemplary ofthe invention and are not to be considered as limiting. Additions,omissions, substitutions, and other modifications can be made withoutdeparting from the scope of the present invention. Accordingly, theinvention is not to be considered as being limited by the foregoingdescription, and is only limited by the scope of the appended claims.

What is claimed is:
 1. A vehicle control method, comprising: recognizingan object present around a vehicle; generating one or a plurality oftarget trajectories along which the vehicle is to travel on the basis ofthe recognized object; automatically controlling driving of the vehicleon the basis of the generated target trajectory; calculating a regionbetween a first virtual line, which passes through a reference pointusing the vehicle as a reference and a first point present in thevicinity of an outer edge of the recognized object, and a second virtualline, which passes through the reference point and a second pointpresent in the vicinity of the outer edge of the object and differentfrom the first point, as a traveling avoidance region that is a regionthrough which traveling of the vehicle should be avoided, and excludingthe target trajectory present in the calculated traveling avoidanceregion from the one or the plurality of target trajectories that weregenerated; and automatically controlling the driving of the vehicle onthe basis of a remaining target trajectory remained without beingexcluded.
 2. The vehicle control method according to claim 1, furthercomprising: optimizing shapes of the first virtual line and the secondvirtual line on the basis of model prediction control.
 3. The vehiclecontrol method according to claim 1, further comprising: calculating arisk region that is a region of a risk distributed around the object,and inputting the risk region to a model that determines the targettrajectory according to the risk region, and generating the one or theplurality of target trajectories on the basis of an output result of themodel to which the risk region has been input.
 4. The vehicle controlmethod according to claim 3, wherein the model is a machine learningmodel learned to output the target trajectory when the risk region isinput.
 5. The vehicle control method according to claim 3, furthercomprising: setting a position at which a potential of the risk is lowerthan a threshold in the vicinity of the outer edge of the object in therisk region as the first point and the second point.
 6. The vehiclecontrol method according to claim 1, wherein, when the object recognizedis a preceding vehicle that is traveling in front of the vehicle in alane on which the vehicle is present, a setting of a region behind thepreceding vehicle as the traveling avoidance region is not performed. 7.The vehicle control method according to claim 1, wherein, when a lanechange of the vehicle is performed, a calculation of the travelingavoidance region, and an exclusion of the target trajectory present inthe traveling avoidance region from the one or the plurality of targettrajectories that were generated, are performed.
 8. A vehicle controldevice, comprising: a hardware processor executing software, hardwareincluding circuitry, or a cooperation of the software and the hardwareconfigured to operate as: a recognition part configured to recognize anobject present around a vehicle; a generating part configured togenerate one or a plurality of target trajectories along which thevehicle is to travel on the basis of the object recognized by therecognition part; and a driving controller configured to automaticallycontrol driving of the vehicle on the basis of the target trajectorygenerated by the generating part, wherein the generating part calculatesa region between a first virtual line, which passes through a referencepoint using the vehicle as a reference and a first point present in thevicinity of an outer edge of the object recognized by the recognitionpart, and a second virtual line, which passes through the referencepoint and a second point present in the vicinity of the outer edge ofthe object and different from the first point, as a traveling avoidanceregion that is a region through which traveling of the vehicle should beavoided, and excludes the target trajectory present in the calculatedtraveling avoidance region from the one or the plurality of targettrajectories that were generated, and the driving controllerautomatically controls driving of the vehicle on the basis of aremaining target trajectory remained without being excluded by thegenerating part.
 9. A non-transitory computer-readable storage medium onwhich a program is stored to execute a computer mounted on a vehicle to:recognize an object present around the vehicle; generate one or aplurality of target trajectories along which the vehicle is to travel onthe basis of the recognized object; automatically control driving of thevehicle on the basis of the generated target trajectory; calculate aregion between a first virtual line, which passes through a referencepoint using the vehicle as a reference and a first point present in thevicinity of an outer edge of the recognized object, and a second virtualline, which passes through the reference point and a second pointpresent in the vicinity of the outer edge of the object and differentfrom the first point, as a traveling avoidance region that is a regionthrough which traveling of the vehicle should be avoided, and excludethe target trajectory present in the calculated traveling avoidanceregion from the one or the plurality of target trajectories that weregenerated; and automatically control the driving of the vehicle on thebasis of a remaining target trajectory remained without being excluded.10. A vehicle control method, comprising: recognizing an object presentaround a vehicle; automatically controlling driving of the vehicle onthe basis of at least the recognized object; calculating a regionbetween a first virtual line, which passes through a reference pointusing the vehicle as a reference and a first point present in thevicinity of an outer edge of the recognized object, and a second virtualline, which passes through the reference point and a second pointpresent in the vicinity of the outer edge of the object and differentfrom the first point, as a traveling avoidance region that is a regionthrough which traveling of the vehicle should be avoided; optimizingshapes of the first virtual line and the second virtual line on thebasis of model prediction control; and automatically controlling thedriving of the vehicle to avoid traveling through the travelingavoidance region.